U.S. patent number 10,548,134 [Application Number 16/244,959] was granted by the patent office on 2020-01-28 for adaptive allocation of temporal resources for massive multiple-input-multiple-output (mimo) in unlicensed frequency bands.
This patent grant is currently assigned to Alcatel Lucent. The grantee listed for this patent is Alcatel Lucent. Invention is credited to Lorenzo Galati Giordano, Adrian J. Garcia Rodriguez, Giovanni Geraci, David Lopez Perez.
United States Patent |
10,548,134 |
Geraci , et al. |
January 28, 2020 |
Adaptive allocation of temporal resources for massive
multiple-input-multiple-output (MIMO) in unlicensed frequency
bands
Abstract
A node is configured for connection to a massive multiple-input,
multiple-output (MIMO) array to provide spatially multiplexed
channels in an unlicensed frequency band. The node includes a
memory configured to store samples of non-spatially filtered
signals received by the node during a first listen-before-talk
(LBT) operation used to acquire the unlicensed frequency band. The
node also includes a processor configured to determine, based on a
number of previously stored samples, a duration of a silent time
interval during which the node collects samples of non-spatially
filtered signals and stores the samples in the memory. The node
further includes a transceiver configured to perform a second LBT
operation to acquire the unlicensed frequency band using a spatial
filter determined based on the samples stored in the memory.
Inventors: |
Geraci; Giovanni (Dublin,
IE), Garcia Rodriguez; Adrian J. (Blanchardstown,
IE), Galati Giordano; Lorenzo (Blanchardstown,
IE), Lopez Perez; David (Blanchardstown,
IE) |
Applicant: |
Name |
City |
State |
Country |
Type |
Alcatel Lucent |
Boulogne-Billancourt |
N/A |
FR |
|
|
Assignee: |
Alcatel Lucent
(Boulogne-Billancourt, FR)
|
Family
ID: |
61868548 |
Appl.
No.: |
16/244,959 |
Filed: |
January 10, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
|
US 20190150152 A1 |
May 16, 2019 |
|
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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15446313 |
Mar 1, 2017 |
10257831 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04B
7/0413 (20130101); H04W 16/14 (20130101); H04B
17/345 (20150115); H04W 72/0453 (20130101); H04L
69/28 (20130101); H04W 74/0808 (20130101) |
Current International
Class: |
H04W
74/04 (20090101); H04W 72/04 (20090101); H04L
29/06 (20060101); H04B 7/0413 (20170101); H04W
16/14 (20090101); H04B 17/345 (20150101); H04W
74/08 (20090101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Notice of Allowance dated May 30, 2019 in U.S. Appl. No.
15/966,913, 21 pages. cited by applicant.
|
Primary Examiner: Phuong; Dai
Parent Case Text
The presentation application is a divisional application of U.S.
patent application Ser. No. 15/446,313, entitled "ADAPTIVE
ALLOCATION OF TEMPORAL RESOURCES FOR MASSIVE
MULTIPLE-INPUT-MULTIPLE-OUTPUT (MIMO) IN UNLICENSED FREQUENCY
BANDS" and filed on 1 Mar. 2017, the entirety of which is
incorporated by reference herein.
Claims
What is claimed is:
1. A method for implementation in a node configured for connection
to a massive multiple-input, multiple-output (MIMO) array to
provide spatially multiplexed channels to user equipment in an
unlicensed frequency band comprising: storing, in a memory in the
node, samples of non-spatially filtered signals received by the
node during at least one previous listen-before-talk (LBT)
operation and at least one previous silent time interval;
discarding a portion of the samples from the memory in response to
the portion being stored in the memory for more than a validity
time interval; and storing, in the memory, additional samples of
non-spatially filtered signals that are collected during a
subsequent silent time interval determined based on a number of
samples in the memory.
2. The method of claim 1, wherein storing the additional samples
comprises collecting the additional samples during the subsequent
silent time interval that is equal to a value sufficient to acquire
a number of samples equal to a difference between the number of
samples stored in the memory and a target number of samples used to
estimate a covariance matrix.
3. The method of claim 2, further comprising: performing a
subsequent LBT operation to acquire the unlicensed frequency band
using a spatial filter determined based on the samples stored in
the memory and the additional samples stored in the memory.
4. The method of claim 3, further comprising at least one of:
decreasing the target number of samples in response to the
subsequent LBT operation being successful and the node acquiring
the unlicensed frequency band; and increasing the target number of
samples in response to the subsequent LBT operation being
unsuccessful and the node failing to acquire the unlicensed
frequency band.
5. The method of claim 2, wherein storing the additional samples
comprises collecting the additional samples during the subsequent
time interval that is equal to the value sufficient to acquire the
number of samples equal to the difference between the target number
and the number of samples stored in the memory in response to the
number of samples stored in the memory being less than the target
number of samples.
6. The method of claim 2, further comprising: setting a duration of
the silent time interval equal to zero in response to the number of
samples stored in the memory being equal to or greater than the
target number of samples.
7. A node configured for connection to a massive multiple-input,
multiple-output (MIMO) array to provide spatially multiplexed
channels in an unlicensed frequency band comprising: a memory
configured to store samples of non-spatially filtered signals
received by the node during at least one previous
listen-before-talk (LBT) operation and at least one previous silent
time interval; and a processor configured to discard a portion of
the samples from the memory in response to the portion being stored
in the memory for more than a validity time interval, and wherein
the memory is configured to store additional samples of
non-spatially filtered signals that are collected during a
subsequent silent time interval determined based on a number of
samples in the memory.
8. The node of claim 7, wherein the processor is configured to
collect the additional samples during the subsequent silent time
interval that is equal to a value sufficient to acquire a number of
samples equal to a difference between the number of samples stored
in the memory and a target number of samples used to estimate a
covariance matrix.
9. The node of claim 8, wherein the processor is configured to
perform a subsequent LBT operation to acquire the unlicensed
frequency band using a spatial filter determined based on the
samples stored in the memory and the additional samples stored in
the memory.
10. The node of claim 9, wherein the processor is configured to:
decrease the target number of samples in response to the subsequent
LBT operation being successful and the node acquiring the
unlicensed frequency band; and increase the target number of
samples in response to the subsequent LBT operation being
unsuccessful and the node failing to acquire the unlicensed
frequency band.
11. The node of claim 8, wherein the processor is configured to
collect the additional samples during the subsequent time interval
that is equal to the value sufficient to acquire the number of
samples equal to the difference between the target number and the
number of samples stored in the memory in response to the number of
samples stored in the memory being less than the target number of
samples.
12. The node of claim 8, wherein the processor is configured to set
a duration of the silent time interval equal to zero in response to
the number of samples stored in the memory being equal to or
greater than the target number of samples.
Description
BACKGROUND
Unlicensed frequency bands are portions of the radiofrequency
spectrum that do not require a license for use and may therefore be
used by any device compliant with regulations to transmit or
receive radiofrequency signals. Wireless communication devices that
transmit or receive signals in licensed or unlicensed frequency
bands are typically referred to as nodes, which may include Wi-Fi
access points that operate according to IEEE 802.11 standards in
the unlicensed spectrum. Nodes also include base stations that
operate in the licensed spectrum according to standards such as
Long Term Evolution (LTE) standards defined by the Third Generation
Partnership Project (3GPP). Base stations that operate according to
LTE can implement supplementary downlink (SDL) channels in the
unlicensed spectrum to provide additional bandwidth for downlink
communications to user equipment that are also communicating with
the base station using channels in a licensed frequency band. The
licensed frequency bands may be referred to as LTE-L bands and the
unlicensed frequency bands may be referred to as LTE-U bands. Base
stations may also operate in the unlicensed frequency bands
according to Licensed Assisted Access (LAA) standards. Base
stations may operate solely in the unlicensed frequency bands
without support in licensed frequency bands, e.g., according to
emerging standards such as MuLTEFire.
In dense networks, channels in the unlicensed frequency bands can
be reused by nodes that operate according to different radio access
technologies (RATs) such as Wi-Fi access points and LTE base
stations. Communication by the nodes that operate according to the
different RATs is coordinated using clear channel assessment
techniques to reduce interference between transmissions by the
different nodes. For example, listen before talk (LBT) coexistence
rules require that each node monitors a channel (e.g., "listens")
to detect energy on the channel prior to transmitting information
on the channel. If the detected energy level is below a threshold
level, the channel is considered clear and the node is free to
transmit on the channel for a predetermined time interval. If the
detected energy level is above the threshold level, which indicates
that the channel is not clear because another node is transmitting
on the channel, the listening node backs off until the energy level
falls below the threshold before making another attempt to acquire
the channel. The energy detection threshold for Wi-Fi is -62
decibel-milliwatts (dBm) and the energy detection threshold for
LTE-U, LAA is -72 dBm, and MuLTEFire is -72 dBm. Wi-Fi nodes may
also perform Wi-Fi preamble decoding on signals with detected
energy levels below the energy detection threshold and above -82
dBm. The Wi-Fi node backs off if it successfully decodes preambles
in transmissions by other Wi-Fi nodes at an energy level between
-62 dBm and -82 dBm.
BRIEF DESCRIPTION OF THE DRAWINGS
The present disclosure may be better understood, and its numerous
features and advantages made apparent to those skilled in the art
by referencing the accompanying drawings. The use of the same
reference symbols in different drawings indicates similar or
identical items.
FIG. 1 is a block diagram of a wireless communication system
according to some embodiments.
FIG. 2 is a plot that illustrates a sum rate achieved by a massive
MIMO array in a sector and a measure of interference suppression as
a function of a number of symbols used to estimate a covariance
matrix according to some embodiments.
FIG. 3 is a block diagram of a node that is configured to be
connected to a massive MIMO array according to some
embodiments.
FIG. 4 is a block diagram that illustrates collection of
non-spatially filtered samples during a sequence of LBT operations
and data transmissions according to some embodiments.
FIG. 5 is a block diagram that illustrates collection of
non-spatially filtered samples during a sequence of LBT operations,
data transmissions, and silent time intervals according to some
embodiments.
FIG. 6 is a flow diagram of a method of collecting non-spatially
filtered samples during adaptively determined silent time intervals
according to some embodiments.
FIG. 7 is a flow diagram of a method for adaptively modifying the
number of samples that are used to calculate covariance matrices
according to some embodiments.
FIG. 8 is a block diagram of a wireless communication system that
performs adaptive allocation of temporal resources to sample
acquisition according to some embodiments.
DETAILED DESCRIPTION
The large number of antennas in a massive multiple-input
multiple-output (MIMO) array used by a node provides a large number
of spatial degrees of freedom that can support spatially
multiplexed communication with multiple user equipment. For
example, a massive MIMO array of N antennas can provide a spatially
multiplexed channel to each of K user equipment as long as
N.gtoreq.K. Additional spatial degrees of freedom can be allocated
to interference suppression to improve coexistence with other
transmitters, such as interfering Wi-Fi nodes. For example,
interference with D single-antenna interfering nodes can be
suppressed by allocating D degrees of freedom to spatial nulls that
are placed onto spatial directions corresponding to the D
interfering nodes, where D.ltoreq.N-K. Placement of the spatial
nulls is determined based on an estimate of a covariance matrix
that represents the channel subspace occupied by the interfering
nodes. The massive MIMO node is therefore required to remain silent
for a time interval to measure signals received from the
interfering nodes. The signals measured during the silent time
interval are then used to determine the covariance matrix.
Allocating a larger silent time interval improves the accuracy of
the estimated covariance matrix, which enables more effective
interference suppression and increases the likelihood of a
successful LBT acquisition of the unlicensed frequency band.
However, data cannot be transmitted during the silent time interval
so increasing the silent time interval reduces the time available
for data transmission.
The performance of a node that uses a massive MIMO array to provide
spatially multiplexed channels to a plurality of user equipment in
an unlicensed frequency band is improved by buffering samples of
non-spatially filtered signals received by the massive MIMO array
during LBT operations performed by the node and determining a
duration of a silent time interval for the node based on a number
of buffered samples of the non-spatially filtered signals. For
example, the number of buffered samples of non-spatially filtered
signals can be compared to a target number of samples. The silent
time interval is set to zero if the number of buffered samples is
greater than or equal to the target number. If the number of
buffered samples is less than the target number, the silent time
interval is set to a value sufficient to acquire a number of
samples that is equal to a difference between the number of
buffered samples and the target number. Some embodiments of the
node retain the buffered samples for a validity time interval.
Buffered samples that were acquired more than the validity time
interval prior to a current time are discarded. The target number
and, in some embodiments, the validity time interval are
dynamically determined based on an outcome of a previous LBT
operation. For example, the target number (and in some embodiments
the validity time interval) can be decreased in response to a
successful outcome of the previous LBT operation and increased in
response to an unsuccessful outcome of the previous LBT
operation.
FIG. 1 is a block diagram of a wireless communication system 100
according to some embodiments. The wireless communication system
100 includes a node 105 that is connected to a massive MIMO array
110 that includes a number (N) of antenna elements that are used
for beamforming of transmitted downlink signals and multiplexing of
received uplink signals. In the illustrated embodiment, the massive
MIMO array 110 is implemented as a two-dimensional array of antenna
elements that are depicted as small squares in FIG. 1. However,
some embodiments of the massive MIMO array 110 are implemented
using other antenna configurations including a linear array of
antenna elements, a cylindrical array of antenna elements, and the
like. The node 105 generates downlink signals for transmission by
the massive MIMO array 110 (e.g., beamforming) and processes uplink
signals that are received by the massive MIMO array 110 (e.g.,
multiplexing), as discussed herein.
The node 105 and the massive MIMO array 110 serve a plurality of
user equipment 111, 112, 113 within a coverage area or cell 115.
The user equipment 111, 112, 113 are collectively referred to
herein as "the user equipment 111-113." The number (N) of antenna
elements in the massive MIMO array 110 is larger than the maximum
number (K.sub.MAX) of user equipment that are served by the node
105 and the massive MIMO array 110. For example, the number (K) of
the user equipment 111-113 served by the massive MIMO array 110 in
the embodiment shown in FIG. 1 is three. The massive MIMO array 110
implements N=64 antenna elements to serve the K=3 user equipment
111-113. Thus, in this embodiment, N>>K. However,
implementations of the massive MIMO array 110 are not limited to
N=64 antenna elements serving three user equipment. Some
embodiments of the massive MIMO array 110 implement more or fewer
antenna elements to serve more or fewer user equipment. For
example, a massive MIMO array can implement hundreds or thousands
of antenna elements to serve tens or hundreds of user equipment,
respectively.
Some embodiments of the node 105 and the massive MIMO array 110
provide wireless connectivity to the user equipment 111-113 in an
unlicensed frequency band. Unlicensed frequency bands are portions
of the radiofrequency spectrum that do not require a license for
use and can therefore be used by any device to transmit or receive
radiofrequency signals. For example, the Unlicensed National
Information Infrastructure (UNII) is formed of portions of the
radio spectrum that include frequency bands in the range of 5.15
GHz to 5.825 GHz. For another example, the industrial, scientific,
and medical (ISM) radio bands are portions of the radio spectrum
that are reserved internationally for unlicensed communication. The
ISM radio bands include bands with a center frequency of 2.4 GHz
and a bandwidth of 100 MHz, a center frequency of 5.8 GHz and a
bandwidth of 150 MHz, and a center frequency of 24.125 GHz and a
bandwidth of 250 MHz, among other frequency bands. Unlicensed
frequency bands can be contrasted to licensed frequency bands that
are licensed to a particular service provider and are only used for
wireless communication that is authorized by the service provider
or license holder.
The node 105 and the massive MIMO array 110 are required to coexist
with other devices that operate according to the same or different
radio access technologies in the unlicensed frequency bands. Some
embodiments of the node 105 and the massive MIMO array 110 operate
according to LTE standards and are configured to coexist with other
devices, such as Wi-Fi nodes 120, 125 that operate within a small
cell 130 that is served by the Wi-Fi node 120. The node 105 can
enhance coexistence with the Wi-Fi nodes 120, 125 by utilizing a
subset of the degrees of freedom of the massive MIMO array 110 to
place spatial nulls 135, 140 on spatial directions corresponding to
the Wi-Fi nodes 120, 125. The node 105 utilizes another (mutually
exclusive) subset of the degrees of freedom of the massive MIMO
array 110 to support spatial channels 145, 150, 155 that are used
for beamforming or multiplexing in the spatial directions
corresponding to the user equipment 111-113. The spatial nulls 135,
140 and the spatial channels 145, 150, 155 are represented by a
spatial filter generated in the node 105 based on a covariance
matrix associated with the Wi-Fi nodes 120, 125, channel state
information from user equipment 111, 112, 113, and the number of
degrees of freedom in the subset that is allocated to support the
spatial nulls 135. Thus, the node 105 supports communication in a
first subspace of spatial channels 145, 150, 155 and generates
spatial nulls 135, 140 in a second subspace of spatial channels
that is orthogonal to the first subspace.
The node 105 is configured to adaptively allocate temporal
resources used to monitor interfering nodes (such as the Wi-Fi
nodes 120, 125) and collect sample measurements of signals received
from the interfering nodes. Some embodiments of the node 105 are
configured buffer samples of non-spatially filtered signals
received by the node 105 during listen-before-talk (LBT) operations
that are used to acquire the unlicensed frequency band. For
example, the node 105 can buffer the samples by storing the samples
in a memory implemented by (or accessible to) the node 105. The
samples of non-spatially filtered signals are also referred to
herein as non-spatially filtered samples. The node 105 uses the
previously stored samples to determine a duration of a silent time
interval during which the node 105 collects samples of
non-spatially filtered signals and buffers the samples, e.g., by
storing the samples in the memory. Portions of the samples can be
discarded from the memory in response to the portion being stored
in the memory for more than a validity time interval. The node 105
can then perform one or more subsequent LBT operations to acquire
the unlicensed frequency band using a spatial filter determined
based on the samples stored in the memory.
FIG. 2 is a plot 200 that illustrates a sum rate achieved by a
massive MIMO array in a sector and a measure of interference
suppression as a function of a number of symbols used to estimate a
covariance matrix according to some embodiments. The left vertical
axis of the plot 200 indicates the sum rate achieved per sector in
megabits per second (Mbps). The right vertical axis of the plot 200
indicates interference (in dBm) received by the 5% of the Wi-Fi
nodes that receive the most interference from the massive MIMO
array during transmissions. The horizontal axis indicates a number
of samples that are used to estimate a covariance matrix that is
used to define a spatial filter for beamforming of the massive MIMO
transmissions and interference suppression from the Wi-Fi nodes.
The curve 205 indicates the achieved sum rate for a massive MIMO
array that includes N=64 antenna elements and the curve 210
indicates the achieved sum rate for a massive MIMO array that
includes N=128 antenna elements. The curve 215 indicates the
interference received by the 5% most-interfered Wi-Fi nodes during
transmissions by a massive MIMO array that includes N=64 antenna
elements and the curve 220 indicates the interference received by
the 5% most-interfered Wi-Fi nodes during transmissions by a
massive MIMO array that includes N=128 antenna elements.
The curve 205 increases due to the improving success rate of the
LBT operation in acquiring the unlicensed frequency band until the
achieved sum rate reaches a plateau at approximately 200 symbols.
Above the threshold of approximately 200 symbols, increasing the
number of symbols used to estimate the covariance matrix provides
diminishing returns because sufficient interference suppression is
achieved at lower numbers of symbols and increasing the number of
symbols increases the overhead by increasing the duration of the
silent time interval needed to acquire the symbols. The curve 210
increases until the achieved sum rate reaches a plateau at
approximately 260 symbols. The achieved sum rate for the curve 210
at the plateau is larger than the corresponding achieved sum rate
for the curve 205 because the larger number of antennas supports
improved beamforming and interference suppression. The plateau for
the curve 210 occurs at a larger number of symbols than the plateau
for the curve 205 because a larger number of symbols are required
to accurately estimate the covariance matrix for the larger number
of antennas.
The curve 215 indicates that the interference received by the 5%
most-interfered Wi-Fi nodes decreases with increasing number of
symbols because of the improved estimation of the covariance
matrix. However, the rate of improvement in the amount of
interference with increasing number of symbols is much higher at
lower numbers of symbols than at higher numbers of symbols. Thus,
the improvement in the interference received by the 5%
most-interfered Wi-Fi nodes reaches a point of diminishing returns
at larger numbers of symbols. The curve 220 also indicates that the
interference received by the 5% most-interfered Wi-Fi nodes
decreases with increasing number of symbols. A comparison of the
curves 215, 220 shows that increasing the number of antennas in the
massive MIMO array provides a marginal reduction in the amount of
interference received by the 5% most-interfered Wi-Fi nodes at
larger numbers of symbols.
The curves 205, 210, 215, 220 illustrate that a node connected to
the massive MIMO array is less likely to acquire the unlicensed
frequency band when the number of samples used to estimate the
covariance matrix is small and interfering nodes are transmitting.
When the number of samples is small, the accuracy of the estimation
of the covariance matrix is insufficient to guarantee a successful
LBT operation, or at least to provide a probability of a successful
LBT operation that is above a threshold. Furthermore, the
interfering nodes receive strong interference from the massive MIMO
array when the node is transmitting because the interference
generated by the massive MIMO array is not accurately suppressed.
On the other hand, the overhead incurred by a large silent time
interval can degrade the effective throughput if the number of
samples that are collected to estimate the covariance matrix is too
large because too much time is allocated to estimating the
covariance matrix and too little time is allocated for data
transmission.
The plot 200 also illustrates that the optimal value for the number
of samples depends on the specific deployment. For example, a
larger number of samples is optimal for a larger MIMO array that
includes more antenna elements. Furthermore, a static assignment of
temporal resources to acquiring samples used for estimation of the
covariance matrix does not result in optimal performance of the
node because the amount of interference that actually needs to be
suppressed varies with time, e.g., because the number of
interfering nodes can increase or decrease due to activation,
deactivation, mobility, environmental conditions, and the like.
FIG. 3 is a block diagram of a node 300 that is configured to be
connected to a massive MIMO array according to some embodiments.
The node 300 is used to implement some embodiments of the node 105
shown in FIG. 1. The node 300 is configured to perform scheduling
of communication using the massive MIMO array in the unlicensed
frequency band in the scheduling block 305. The node 300 is
configured to perform transmission of data in a first subspace of
spatial channels while performing spatial nulling on a second
subspace of spatial channels in the transmission block 310. Thus,
the node 300 is configured to perform beamforming of downlink
transmissions concurrently with spatial nulling. The node 300 is
also able to perform multiplexing of uplink transmissions
concurrently with spatial nulling using blocks that operate
analogously to the scheduling block 305 and the transmission block
310.
The node 300 estimates a covariance matrix for one or more
interfering nodes in the block 315. To estimate the covariance
matrix, the node 300 interrupts or bypasses transmission during a
silent time interval and monitors signals received by the massive
MIMO array during the silent time interval. The duration of the
silent time interval (which can be zero, indicating that there is
no silent time interval for a particular iteration of the
scheduling/transmission process) is determined based on a number of
previously received and stored samples. The stored samples can be
acquired during a previous silent time interval or the stored
samples can be non-spatially filtered samples acquired during an
LBT operation. For example, during an LBT operation, the node 300
can acquire non-spatially filtered samples during a monitoring
interval prior to applying a spatial filter and determining whether
any interfering nodes are present based on the spatially filtered
samples.
The node 300 uses the stored samples to generate channel state
information for interfering nodes, such as Wi-Fi nodes. The channel
state information is used to suppress interference during
subsequent data transmission and reception stages, e.g., after the
silent time interval has expired. In some embodiments, the channel
state information is used to generate a covariance matrix for the
interfering nodes. For example, let z[m].di-elect
cons..sup.N.times.1 denote the sample received by the node 300 at
symbol interval min the N antenna elements in the massive MIMO
array. The node 300 monitors the received signals during an
interval of M symbols to collect a corresponding M samples and
generates an estimate of their aggregate channel covariance matrix,
Z.di-elect cons..sup.N.times.N, as:
.times..function..times..function. ##EQU00001## where ( ).sup.H
denotes the conjugate transpose operation. The covariance matrix
represents spatial channels between the massive MIMO array and the
interfering nodes within its coverage region.
Spatial resources are allocated to downlink spatial channels and
spatial nulls in the block 320. The node determines a number of
spatial nulls that are allocated to interference suppression of the
interfering nodes represented in the channel covariance matrix.
Once the number of spatial nulls has been determined, the node 300
generates a spatial filter that represents the spatial channels
used for beamforming downlink transmissions and the spatial nulls.
The spatial filter can be applied to signals during the LBT
operation and the data transmission phases.
The node 300 schedules users for communication in the unlicensed
frequency band in the block 325. In the illustrated embodiment,
data transmission for the scheduled users is contingent upon the
node acquiring the unlicensed frequency band by performing a
successful LBT operation.
The node 300 performs an LBT operation in the block 330. The LBT
operation is performed on signals that are filtered on the basis of
the spatial filter generated in block 320. For example, the node
300 first acquires non-spatially filtered samples by monitoring
signals received while the node 300 is not transmitting during the
LBT operation 330. The node 300 then applies a spatial filter to
generate spatially filtered samples from the non-spatially filtered
samples. Applying the spatial filter during the LBT operation is
referred to as "enhanced LBT." To begin the LBT operation, the node
300 interrupts or bypasses transmissions and monitors non-spatially
filtered signals received by the massive MIMO array. The
non-spatially filtered signals are used to generate non-spatially
filtered samples. The non-spatially filtered samples are stored
during the LBT operation 330 so that the non-spatially filtered
samples collected during the LBT operation 330 can be used to
estimate the covariance matrix at block 305, as indicated by the
arrow 333.
The node 300 applies spatial filter to the non-spatially filtered
samples in the block 330 to remove signals received on the subspace
of channels corresponding to the spatial nulls. Thus, signals
received from interfering nodes in the spatial directions
associated with the spatial nulls are not included in the signals
that are used to determine whether a detected energy level in the
received signals is above a threshold indicating that the
unlicensed frequency band is occupied by another interfering node.
If the detected energy level is above the threshold, the LBT
operation 330 is unsuccessful and the node does not acquire the
unlicensed frequency band. The node 300 does not perform the
operations in the blocks 335, 340, 345 if the LBT operation 330 is
unsuccessful. If the detected energy level is below the threshold,
the LBT operation 330 is successful and the node 300 acquires the
unlicensed frequency band.
The node 300 transmits (at block 335) a request for user equipment
to send pilot signals, which the node 300 can use for channel
estimation. Some embodiments of the node 300 broadcast the request
on a spatial channel subspace that is orthogonal to directions of
the strongest interfering nodes. For example, the node 300 can
broadcast the request using the spatial filter determined in block
320. The node 300 receives pilot signals in block 340 and uses the
pilot signals to estimate channel state information for the user
equipment that transmitted the pilot signals. Some embodiments of
the node 300 apply the spatial filter determined in block 320 to
the received pilot signals so that signals transmitted by
interfering nodes are suppressed by the spatial nulls represented
by the spatial filter.
The node 300 performs the scheduled data transmissions in block
345. The data transmissions are precoded based on the spatial
filter determined in block 320 to prevent the data transmissions
from causing interference at the interfering nodes.
FIG. 4 is a block diagram that illustrates collection of
non-spatially filtered samples a sequence 400 of LBT operations and
data transmissions according to some embodiments. The sequence 400
is implemented in a node such as some embodiments of the node 105
shown in FIG. 1 and the node 300 shown in FIG. 3. The sequence 400
includes LBT operations 401, 402, 403 that are collectively
referred to herein as "the LBT operations 401-403." The sequence
400 also includes data transmissions 405, 410.
The LBT operation 401 includes a listening phase 415 in which the
node interrupts or bypasses data transmissions on an unlicensed
frequency band and acquires non-spatially filtered samples by
monitoring signals received from interfering nodes in the
unlicensed frequency band. For example, the node can acquire z[m]
samples of signals transmitted by interfering nodes such as Wi-Fi
nodes and received during m symbol intervals by a massive MIMO
array connected to the node. The z[m] samples are acquired without
applying a spatial filter to the received signals and are therefore
referred to as non-spatially filtered samples. The node stores the
z[m] samples in a memory at block 425. The LBT operation 401 also
includes a comparison phase 420 in which the node applies a
previously determined spatial filter to the z[m] samples and
determines a total energy received in the spatially filtered
signal. If the total received energy is above a threshold,
indicating that interfering nodes are present on the monitored
spatial channels defined by the spatial filter, the LBT operation
401 is unsuccessful and the node does not acquire the unlicensed
frequency band. If the total received energy is below the
threshold, indicating that interfering nodes are not present on the
monitored spatial channels, the LBT operation 401 is successful and
the node acquires the unlicensed frequency band.
Data transmission 405 is performed on the spatial channels defined
by the spatial filter if the previous LBT operation 401 was
successful. Otherwise, the data transmission 405 is bypassed if the
previous LBT operation 401 was not successful. Data transmission
405 can include beamforming of downlink signals or multiplexing of
uplink signals. At block 430, the node estimates a covariance
matrix using the samples stored at block 425 concurrently with the
data transmission 405 (if performed). The node also calculates a
spatial filter that defines spatial channels (e.g., for beamforming
or multiplexing) and spatial nulls for interference suppression
based on the covariance matrix. The spatial filter is then used in
a subsequent LBT operation 402.
Although not explicitly shown in FIG. 4, the LBT operation 402
includes a listening phase in which the node interrupts or bypasses
data transmissions on an unlicensed frequency band and acquires
non-spatially filtered samples by monitoring signals received from
interfering nodes in the unlicensed frequency band. The node stores
the non-spatially filtered samples in a memory at block 435. The
LBT operation 402 also includes a comparison phase in which the
node applies a previously determined spatial filter to the
non-spatially filtered samples and determines a total energy
received in the spatially filtered signal. If the total received
energy is above a threshold, indicating that interfering nodes are
present on the monitored spatial channels indicated by the spatial
filter, the LBT operation 402 is unsuccessful and the node does not
acquire the unlicensed frequency band. If the total received energy
is below the threshold, indicating that interfering nodes are not
present on the monitored spatial channels, the LBT operation 402 is
successful and the node acquires the unlicensed frequency band.
Data transmission 410 is performed on the spatial channels defined
by the spatial filter if the previous LBT operation 402 was
successful. Otherwise, the data transmission 410 is bypassed if the
previous LBT operation 402 was not successful. Data transmission
410 can include beamforming of downlink signals or multiplexing of
uplink signals. At block 440, the node estimates a covariance
matrix using the samples stored at block 435 concurrently with the
data transmission 410 (if performed). The node also calculates a
spatial filter that defines spatial channels (e.g., for beamforming
or multiplexing) and spatial nulls for interference suppression
based on the covariance matrix. The spatial filter is then used in
a subsequent LBT operation 403.
In some cases, the number of samples collected in the LBT
operations 401-403 are insufficient to provide an optimal estimate
of the covariance matrix, as discussed herein with regard to FIG.
2. Stored samples can also be discarded after a validity time
interval, which further reduces the number of samples available to
estimate the covariance matrix. The node is therefore configured to
dynamically allocate silent time intervals to collect additional
non-spatially filtered samples that can be used in combination with
the samples collected during the LBT operations 401-403 to estimate
the covariance matrices.
FIG. 5 is a block diagram that illustrates collection of
non-spatially filtered samples a sequence 500 of LBT operations,
data transmissions, and silent time intervals according to some
embodiments. The sequence 500 is implemented in a node such as some
embodiments of the node 105 shown in FIG. 1 and the node 300 shown
in FIG. 3. The sequence 500 includes LBT operations 501, 502, 503
that are collectively referred to herein as "the LBT operations
501-503." The sequence 500 also includes data transmissions 505,
510.
As discussed herein with regard to FIG. 4, the node can acquire and
store non-spatially filtered samples during the LBT operation 501
and the non-spatially filtered samples can be stored in a memory
515. In the illustrated embodiment, the memory 515 includes
previously stored non-spatially filtered samples 520 and the
non-spatially filtered samples 525 that were acquired during the
LBT operation 501. The total number of samples in the memory 515 is
currently above a number 530 of samples that are used to estimate
covariance matrices for spatially filtering samples during the LBT
operations 501-503 and data transmissions 505, 510. However, a
validity time interval for the previously acquired samples 520
expires during the data transmission 505, or at least prior to the
subsequent LBT operation 502. The previously acquired samples 520
are therefore discarded from the memory 515, which now stores less
than the number 530 that is needed to estimate covariance
matrices.
In response to determining that the number of samples stored in the
memory 515 is less than the number 530 of samples used to estimate
the covariance matrices, the node schedules a silent time interval
535 during which the node interrupts or bypasses transmissions and
monitors signals received from interfering nodes. The node collects
and stores the non-spatially filtered samples 540 during the silent
time interval 535. The node determines the duration of the silent
time interval 535 so that the silent time interval 535 is long
enough to permit the node to acquire a number of non-spatially
filtered samples 540 that is equal to a difference between the
number of currently stored samples 525 and the number 530 of
samples that are used to estimate the covariance matrices. Thus,
the node is able to acquire a sufficient number of samples to
estimate the covariance matrices using a minimal duration of the
silent time interval 535.
The node performs the LBT operation 502 using a spatial filter
determined based on the stored samples 525, 540. For example, the
node acquires non-spatially filtered samples during a listening
phase of the LBT operation 502 and applies the spatial filter
determined based on the stored samples 525, 540 during a comparison
phase that is used to determine whether the LBT operation is
successful or not. The node also stores the acquired non-spatially
filtered samples 545 in the memory for subsequent use determining
covariance matrices.
A validity time interval 548 for the previously acquired samples
525 expires during the data transmission 510, or at least prior to
the subsequent LBT operation 503. The previously acquired samples
525 are therefore discarded from the memory 515. However, the
memory 515 still stores a number of samples 540, 545 that is
greater than or equal to the number 530 that is needed to estimate
covariance matrices. Consequently, the node does not need to
acquire any additional samples to estimate the covariance matrices.
The node therefore does not introduce a silent time interval
between the data transmission 510 and the subsequent LBT operation
503. For example, the node can set the duration of the silent time
interval equal to zero.
The node performs the LBT operation 503 using a spatial filter
determined based on a portion 550 of the stored samples 540, 545
that includes a number of samples equal to the number 530. For
example, the node acquires non-spatially filtered samples during a
listening phase of the LBT operation 503 and applies the spatial
filter determined based on the portion 550 of the stored samples
540, 545 during a comparison phase that is used to determine
whether the LBT operation is successful or not. The node also
stores the acquired non-spatially filtered samples 555 in the
memory for subsequent use determining covariance matrices.
FIG. 6 is a flow diagram of a method 600 of collecting
non-spatially filtered samples during adaptively determined silent
time intervals according to some embodiments. The method 600 is
implemented in a node such as some embodiments of the node 105
shown in FIG. 1 and the node 300 shown in FIG. 3.
At block 605, the node counts the number of samples that are stored
in a memory. At decision block 610, the node determines whether the
number of stored samples is less than a threshold that indicates a
number of samples that are used to calculate a covariance matrix,
which is then used to determine a spatial filter for a massive MIMO
array. If the number is greater than or equal to the threshold, the
method 600 flows to block 615. Otherwise, if the number is less
than the threshold, the method 600 flows to block 620.
At block 620, the node determines a silent time interval based on a
difference between the number of stored samples and the threshold
value. For example, the node can determine a duration of the silent
time interval that is sufficient to allow the node to acquire a
number of non-spatially filtered samples that is equal to the
difference between the number of stored samples and the threshold
value. At block 625, the node collects the non-spatially filtered
samples during the silent time interval and stores the collected
samples in the memory. The method 600 then flows to block 615.
At block 615, the node performs an LBT operation using a spatial
filter determined by a covariance matrix that is defined by samples
previously stored in the memory. As discussed herein, the LBT
operation includes a listening phase in which the node acquires
additional non-spatially filtered samples prior to applying a
spatial filter to the acquired samples, e.g., in a comparison
phase. At block 630, the node stores the additional non-spatially
filtered samples acquired during the LBT operation in the memory.
At block 635, the node removes invalid samples from the memory. For
example, samples that have been in the memory for longer than a
validity time interval are removed from the memory. The method 600
then flows back to block 605 to begin another iteration.
FIG. 7 is a flow diagram of a method 700 for adaptively modifying
the number of samples that are used to calculate covariance
matrices according to some embodiments. The method 700 is
implemented in a node such as some embodiments of the node 105
shown in FIG. 1 and the node 300 shown in FIG. 3.
At block 705, the node performs an LBT operation on the basis of a
spatial filter that is determined using a covariance matrix
generated using a current number of samples. In some embodiments,
the current number of samples is set using a conservative approach
that initializes the number of samples to a large value so that the
estimate of the covariance matrix is accurate. Alternatively, a
greedy approach can be used to initialize the current number of
samples to a relatively small value.
At decision block 710, the node determines whether the LBT
operation was successful and whether the node acquired the
unlicensed frequency band. For example, the node can determine the
amount of energy detected on spatial channels defined by the
spatial filter and compare the detected energy to a threshold
value. The LBT operation is successful if the detected energy is
below the threshold value and unsuccessful if the detected energy
is above the threshold value. If the LBT operation is successful,
the method 700 flows to block 715. If the LBT operation is
unsuccessful, the method 700 flows to block 720.
At block 715, the node decreases the number of samples that are
used for estimating the covariance matrix. Decreasing the number of
samples decreases the overhead incurred to acquire samples used to
estimate the covariance matrix. In some embodiments, the number of
samples is selectively decreased based on a predicted outcome of
the LBT operation if it had been performed using a subset of the
available samples that includes the decreased number of samples.
The number of samples is decreased if the predicted outcome of the
LBT operation is a successful result and the number of samples is
maintained if the predicted outcome of the LBT operation is
unsuccessful result.
At block 720, the node increases the number of samples that are
used for estimating the covariance matrix. Although increasing the
number of samples increases the overhead incurred to acquire
samples, increasing the number of samples also increases the
accuracy of the estimated covariance matrix and increases the
likelihood that a subsequent LBT operation will be successful. In
some embodiments, the number of samples is selectively increased
based on a predicted outcome of the LBT operation assuming that the
LBT operation had been performed using a larger number of samples.
For example, if the memory includes additional samples that were
not used to estimate the covariance matrix in block 705, the node
can generate a modified covariance matrix using a large number of
samples. If the predicted outcome of the LBT operation using the
modified covariance matrix is successful, the number of samples
used to perform the covariance estimation is increased. If the
predicted outcome of the LBT operation using the modified
covariance matrix is still unsuccessful, the number of samples used
to perform the covariance estimation is maintained. Furthermore, an
unsuccessful predicted outcome of the LBT operation using the
modified covariance matrix could indicate that the LBT operation
was unsuccessful because the samples were outdated and not because
there was an insufficient number of samples. In that case, a
validity time interval applied to the samples can be decreased.
In some embodiments, filters and a hysteresis margin are applied in
method 700 to mitigate possible ping-pong effects.
FIG. 8 is a block diagram of a wireless communication system 800
that performs adaptive allocation of temporal resources to sample
acquisition according to some embodiments. The wireless
communication system 800 includes a node 805 that is connected to a
massive MIMO array 810 for providing wireless connectivity to user
equipment 815, 820 using spatial channels 825, 830. The node 805
can also place spatial nulls 835 on to spatial directions indicated
by a location of an interfering node 840.
The node 805 includes a transceiver 845 for transmitting and
receiving signals such as signals that are exchanged between the
node 805 and the massive MIMO array 810. The transceiver 845 can be
implemented as a single integrated circuit (e.g., using a single
ASIC or FPGA) or as a system-on-a-chip (SOC) that includes
different modules for implementing the functionality of the
transceiver 845. The node 805 also includes a processor 850 and a
memory 855. The processor 850 can be used to execute instructions
stored in the memory 855 and to store information in the memory 855
such as the results of the executed instructions. For example, the
memory 855 can store non-spatially filtered samples collected
during an LBT operation or a silent time interval. The transceiver
845, the processor 850, and the memory 855 can therefore be
configured to implement some embodiments of the node 300 shown in
FIG. 3. The transceiver 845, the processor 850, and the memory 855
can also be configured to perform some embodiments of the method
600 shown in FIG. 6 and the method 700 shown in FIG. 7.
In some embodiments, certain aspects of the techniques described
above may implemented by one or more processors of a processing
system executing software. The software comprises one or more sets
of executable instructions stored or otherwise tangibly embodied on
a non-transitory computer readable storage medium. The software can
include the instructions and certain data that, when executed by
the one or more processors, manipulate the one or more processors
to perform one or more aspects of the techniques described above.
The non-transitory computer readable storage medium can include,
for example, a magnetic or optical disk storage device, solid state
storage devices such as Flash memory, a cache, random access memory
(RAM) or other non-volatile memory device or devices, and the like.
The executable instructions stored on the non-transitory computer
readable storage medium may be in source code, assembly language
code, object code, or other instruction format that is interpreted
or otherwise executable by one or more processors.
A computer readable storage medium may include any storage medium,
or combination of storage media, accessible by a computer system
during use to provide instructions and/or data to the computer
system. Such storage media can include, but is not limited to,
optical media (e.g., compact disc (CD), digital versatile disc
(DVD), Blu-Ray disc), magnetic media (e.g., floppy disc, magnetic
tape, or magnetic hard drive), volatile memory (e.g., random access
memory (RAM) or cache), non-volatile memory (e.g., read-only memory
(ROM) or Flash memory), or microelectromechanical systems
(MEMS)-based storage media. The computer readable storage medium
may be embedded in the computing system (e.g., system RAM or ROM),
fixedly attached to the computing system (e.g., a magnetic hard
drive), removably attached to the computing system (e.g., an
optical disc or Universal Serial Bus (USB)-based Flash memory), or
coupled to the computer system via a wired or wireless network
(e.g., network accessible storage (NAS)).
Note that not all of the activities or elements described above in
the general description are required, that a portion of a specific
activity or device may not be required, and that one or more
further activities may be performed, or elements included, in
addition to those described. Still further, the order in which
activities are listed are not necessarily the order in which they
are performed. Also, the concepts have been described with
reference to specific embodiments. However, one of ordinary skill
in the art appreciates that various modifications and changes can
be made without departing from the scope of the present disclosure
as set forth in the claims below. Accordingly, the specification
and figures are to be regarded in an illustrative rather than a
restrictive sense, and all such modifications are intended to be
included within the scope of the present disclosure.
Benefits, other advantages, and solutions to problems have been
described above with regard to specific embodiments. However, the
benefits, advantages, solutions to problems, and any feature(s)
that may cause any benefit, advantage, or solution to occur or
become more pronounced are not to be construed as a critical,
required, or essential feature of any or all the claims. Moreover,
the particular embodiments disclosed above are illustrative only,
as the disclosed subject matter may be modified and practiced in
different but equivalent manners apparent to those skilled in the
art having the benefit of the teachings herein. No limitations are
intended to the details of construction or design herein shown,
other than as described in the claims below. It is therefore
evident that the particular embodiments disclosed above may be
altered or modified and all such variations are considered within
the scope of the disclosed subject matter. Accordingly, the
protection sought herein is as set forth in the claims below.
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